Realistic Inference Research Articles

Analysis Of Students' Cognitive Process Through Minimum Competency Assessment Mathematics Question in Grade 8^th in Junior High School

<p>This study aims to analyze students' cognitive processes through minimum competency assessment questions. This study uses a qualitative research approach with a descriptive method to describe the mental process of students using minimum competency assessment questions that are carried out at the beginning of learning. The results showed that 13 out of 22 students could use previous knowledge and explain how they used it to understand the text, 21 could identify important and relevant ideas, and as many as 9 students were able to make realistic inferences and cite supporting evidence. Most students cannot yet think mathematically and can solve the given problems. This study also found that as many as 19 students were able to define all vocabulary and be able to explain it. Eighteen participants could not understand the multimodal text, and showed that 11 students were quite able to show how to understand the text even though it was incomplete. Teachers can use this data for initial data as a reference for implementing learning strategies for future teaching.</p>

Rural Consumers’ Satisfaction towards Payment Banks

Purpose: The study aims to investigate the satisfaction towards rural payment bank users underpins, namely flexibility, security, trust and rewards on the one hand and satisfaction on the other, as well as the mediation effects of service quality. Methodology:The researcher designed a structured questionnaire for collection of primary data on satisfaction towards rural payment bank users in Tamil Nadu. Convenience sampling method was used. A total of 225 rural customers provided the data for the study. Percentage analysis, multiple regression analysis and One-way analysis were used to measure the satisfaction. Findings: There is a significant influence of demographic variables on satisfaction level of rural payment banks user in Tamil Nadu. The rural payment bank users were satisfied with the services provided by the service provider and its saves time and make them comfort in payment from anywhere to any destination at any time. Realistic Inference: The study shows that the rural payment bank users are satisfied towards the services rendered by payment banks and introducer RBI the bankers’ bank in India.

The landscape of nucleotide diversity in Drosophila melanogaster is shaped by mutation rate variation

What shapes the distribution of nucleotide diversity along the genome? Attempts to answer this question have sparked debate about the roles of neutral stochastic processes and natural selection in molecular evolution. However, the mechanisms of evolution do not act in isolation, and integrative models that simultaneously consider the influence of multiple factors on diversity are lacking; without them, confounding factors lurk in the estimates. Here we present a new statistical method that jointly infers the genomic landscapes of genealogies, recombination rates and mutation rates. In doing so, our model captures the effects of genetic drift, linked selection and local mutation rates on patterns of genomic variation. We then formalize a causal model of how these micro-evolutionary mechanisms interact, and cast it as a linear regression to estimate their individual contributions to levels of diversity along the genome. Our analyses reclaim the well-established signature of linked selection in Drosophila melanogaster, but we estimate that the mutation landscape is the major driver of the genome-wide distribution of diversity in this species. Furthermore, our simulation results suggest that in many evolutionary scenarios the mutation landscape will be a crucial factor shaping diversity, depending notably on the genomic window size. We argue that incorporating mutation rate variation into the null model of molecular evolution will lead to more realistic inferences in population genomics.

Fine-scale functional connectivity of two syntopic pond-breeding amphibians with contrasting life-history traits: an integrative assessment of direct and indirect estimates of dispersal

Assessing patterns of functional connectivity among amphibian demes is crucial to unravel their population dynamics and prevent their isolation and eventual extinction. Integrative studies based on direct (capture-mark-recapture) and indirect (genetic) estimates of dispersal provide robust, biologically realistic inferences on population structure and connectivity, with applications for conservation efforts. We focused on two pond-breeding amphibians with contrasting life-history traits: the short-lived, semi-arboreal Hyla molleri and the long-lived, fossorial Pelobates cultripes. We PIT-tagged 2150 individuals of both species in two ponds (Laguna and Gravera, separated by 700 m) and monitored them from 2009 to 2021 to document the frequency and spatial extent of dispersal events. In addition, we genotyped individuals from these and two additional breeding populations at a maximum distance of 5 km with 15–16 microsatellites to characterize fine-scale patterns of genetic structure. We detected dispersal events connecting Laguna and Gravera in both species, albeit at low frequencies (4.8% and 7.7% of recaptured individuals of H. molleri and P. cultripes, respectively). However, both species were capable of covering long distances, with individual accumulated displacements up to 3.5 km (Hyla) and 1.8 km (Pelobates). Breeding populations > 2 km apart were genetically differentiated, indicating lower connectivity at this spatial scale. Estimates of pairwise migration rates differed between species and were asymmetrical, with different ponds representing “source” populations contributing more migrants to other populations in each species. We discuss the role of differences in life history traits and ecological preferences in shaping population dynamics in the two species and highlight management implications of our results.

Population projections from holey matrices: Using prior information to estimate rare transition events

Population projection matrices are a common means for predicting short- and long-term population persistence for rare, threatened and endangered species. Data from such species can suffer from small sample sizes and consequently miss rare demographic events resulting in incomplete or biologically unrealistic life cycle trajectories. Matrices with missing values (zeros; e.g., no observation of seeds transitioning to seedlings) are often patched using prior information from the literature, other populations, time periods, other species, best guess estimates, or are sometimes even ignored. To alleviate this problem, we propose using a multinomial-Dirichlet model for parameterizing transitions and a Gamma for reproduction to patch missing values in these holey matrices. This formally integrates prior information within a Bayesian framework and explicitly includes the weight of the prior information on the posterior distributions. We show using two real data sets that the weight assigned to the prior information mainly influences the dispersion of the posteriors, the inclusion of priors results in irreducible and ergodic matrices, and more biologically realistic inferences can be made on the transition probabilities. Because the priors are explicitly stated, the results are reproducible and can be re-evaluated if alternative priors are available in the future.

Visual analytic based ship collision probability modeling for ship navigation safety

This study presents a tangible visual analytic tool to analyse maritime traffic in spatio-temporal basis using AIS data. This novel approach helps in understanding the macroscopic safety structure of both fairways and individual ships with evidences in microscopic level. Qualification of our system is demonstrated with 7-days AIS trajectory collected from Mexican Gulf. We find out that spatio-temporal position pattern of encountered ships in Port Houston varies over time, significantly. In addition, the spatial distribution of ship accidents coincide with proposed near-miss density areas. Furthermore, proposed tool is capable of capturing real accident cases. Field experiments with domain experts have demonstrated that our approach helps in making realistic inferences about navigational safety behaviour of both individual vessel and water area.

ProtASR2: Ancestral reconstruction of protein sequences accounting for folding stability

Abstract The ancestral sequence reconstruction (ASR) is a molecular evolution technique that provides applications to a variety of fields such as biotechnology and biomedicine. To infer ancestral sequences with realistic biological properties, the accuracy of ASR methods is crucial. We previously developed an ASR framework for proteins, called ProtASR, which is based on our site‐specific stability‐constrained substitution (SCS) model with selection on protein folding stability against both unfolding and misfolding. This model improved the empirical substitution models traditionally applied in ASR without increasing the computational complexity. However, it adopted a global exchangeability matrix, an approximation that we overcome here by considering site‐specific exchangeability matrices based on the Halpern–Bruno approach. Here we present ProtASR2, a new version of our ASR framework that implements novel SCS models of protein evolution, namely mean‐field (MF) and wild‐type (WT). ProtASR2 under MF and WT SCS models outperforms empirical models and previous SCS models in terms of goodness of fit and site‐specific distributions of amino acids. Importantly, the framework infers ancestral sequences with more realistic predicted folding stability with respect to simulated sequences, while empirical, CAT and other SCS models tend to overestimate the folding stability. We applied ProtASR2 to explore the evolution of two protein families present in diverse Prokaryota and found fluctuations of protein stability over time in both families. ProtASR2 is available from https://github.com/miguelarenas/protasr and the new SCS models are also available from https://github.com/ugobas/protevol. Use of ProtASR2 will allow more realistic inferences of ancestral proteins in terms of folding stability with respect to those based on traditional empirical and CAT substitution models of protein evolution.

Constrained inference in sparse coding reproduces contextual effects and predicts laminar neural dynamics.

When probed with complex stimuli that extend beyond their classical receptive field, neurons in primary visual cortex display complex and non-linear response characteristics. Sparse coding models reproduce some of the observed contextual effects, but still fail to provide a satisfactory explanation in terms of realistic neural structures and cortical mechanisms, since the connection scheme they propose consists only of interactions among neurons with overlapping input fields. Here we propose an extended generative model for visual scenes that includes spatial dependencies among different features. We derive a neurophysiologically realistic inference scheme under the constraint that neurons have direct access only to local image information. The scheme can be interpreted as a network in primary visual cortex where two neural populations are organized in different layers within orientation hypercolumns that are connected by local, short-range and long-range recurrent interactions. When trained with natural images, the model predicts a connectivity structure linking neurons with similar orientation preferences matching the typical patterns found for long-ranging horizontal axons and feedback projections in visual cortex. Subjected to contextual stimuli typically used in empirical studies, our model replicates several hallmark effects of contextual processing and predicts characteristic differences for surround modulation between the two model populations. In summary, our model provides a novel framework for contextual processing in the visual system proposing a well-defined functional role for horizontal axons and feedback projections.

A natural field experiment examining the joint role of audit partner leadership and subordinates’ knowledge in fraud brainstorming

Research shows that audit partner leadership is critical to achieving fraud brainstorming objectives. We examine how partner leadership and subordinate knowledge jointly influence brainstorming processes and outcomes. We conduct a natural field experiment that manipulates partner leadership during actual brainstorming sessions to leverage naturally-occurring differences in the knowledge levels of managers versus seniors. Our design allows us to examine how knowledge differences within the organization influence judgments on actual engagements, thereby facilitating uniquely realistic inferences about partner leadership in interactive brainstorming. We predict and find that quality-differentiated leadership improves the mental representations of fraud risk for seniors, but not managers. Consistent with theory around shared mental models, these changes are, in turn, associated with the engagement team's planned fraud risk responses. Further analyses reveal that our leadership prompts are relatively more novel for seniors than managers, supporting the notion that seniors have more room for improvement in their mental representations than managers.

Machine Learning of Noise in LHD Thomson Scattering System

Manual uncertainty propagation from possible noise sources has often been adopted for data analysis in many fields of science, including the analysis of Thomson scattering measurement data in fusion plasma science. However, it is not possible to perfectly model all the noise sources and their distributions. In this work, we propose a more data-driven approach for the noise modeling of multichannel measurement systems. We directly modeled the noise distribution by tractable density distributions parameterized with neural networks and trained their weights from a vast amount of measurement data. We demonstrated an application of this method in Thomson scattering measurement data for the Large Helical Device project. This method enabled us to make a realistic inference even without sufficient prior knowledge about the noise.

A Field Experiment Examining Audit Subordinates' Knowledge and a Partner-Led Intervention in Fraud Brainstorming

In a field experiment, we leverage differences in audit subordinates’ job-relevant knowledge about clients, fraud brainstorming objectives, and “normal” audit partner leadership to explore whether and how knowledge moderates the effect of a partner-led intervention on brainstorming processes and outcomes. Our research design allows us to examine how differences in subordinate knowledge (between seniors and managers) influence auditors’ judgment processes on actual audit engagements, thereby facilitating uniquely realistic inferences about the conditions under which prompt-based interventions influence brainstorming. We develop and test a theoretical model that articulates an indirect effect of quality-differentiated partner leadership on brainstorming outcomes through changes in subordinates’ mental representations of fraud risk, conditional on subordinate knowledge. We manipulate partner leadership in brainstorming sessions for actual audit engagements and induce quality-differentiated leadership in our treatment group. We measure changes in mental representations using retrospective recalls of targeted professional skepticism, the number of new fraud risks identified, and changes in fraud risk assessments as a result of brainstorming. We predict and find an effect of quality-differentiated partner leadership on changes in the mental representations of seniors, but not managers. We also predict and find an indirect effect on fraud risk response outcomes (i.e., planned procedures) through changes in mental representations of seniors, but not managers. Both effects are stronger for seniors than managers. Our findings suggest knowledge differences as an explanatory factor that can guide the interpretation of, and potentially reconcile, prior mixed findings regarding incremental benefits of interventions in teams that are already brainstorming interactively.

A Bayesian model for quantifying the change in mortality associated with future ozone exposures under climate change.

Climate change is expected to have many impacts on the environment, including changes in ozone concentrations at the surface level. A key public health concern is the potential increase in ozone-related summertime mortality if surface ozone concentrations rise in response to climate change. Although ozone formation depends partly on summertime weather, which exhibits considerable inter-annual variability, previous health impact studies have not incorporated the variability of ozone into their prediction models. A major source of uncertainty in the health impacts is the variability of the modeled ozone concentrations. We propose a Bayesian model and Monte Carlo estimation method for quantifying health effects of future ozone. An advantage of this approach is that we include the uncertainty in both the health effect association and the modeled ozone concentrations. Using our proposed approach, we quantify the expected change in ozone-related summertime mortality in the contiguous United States between 2000 and 2050 under a changing climate. The mortality estimates show regional patterns in the expected degree of impact. We also illustrate the results when using a common technique in previous work that averages ozone to reduce the size of the data, and contrast these findings with our own. Our analysis yields more realistic inferences, providing clearer interpretation for decision making regarding the impacts of climate change.

It's all relative: Spatial positioning of parties and ideological shifts

AbstractThe usefulness of the general class of spatial econometric models, which relaxes the assumption that the observations are independent, has only recently been realised. One particularly fruitful application includes models of parties' ideological change as well as the electoral consequences of party competition. In these studies, scholars can explicitly model the spatial interconnectedness of political parties in theoretically pleasing ways, producing inferences that are consistent with formal models of party competition, but are beyond the grasp of traditional ordinary least squares (OLS) regression models. To illustrate these benefits, this article replicates Adams and Somer‐Topcu's 2009 study of parties' responses to ideological shifts by rival parties to show that appropriately modeling patterns of interconnectivity between parties via weights matrices provides more realistic inferences that are more consistent with formal models of party competition.

Technoeconomic Projections with Artificial Neural Networks using an Ensemble of Sparsely-Sampled Bootstrapped Data

Aims: The present study refers to developing an artificial neural network (ANN) that can be designed exclusively for ex ante forecasting in technoeconomic contexts using an ensemble set of sparse and insufficient sampled-data availed ex post. Study Design: In general, the samples in a data set of technoeconomic structures would largely be limited in number due to sparse-sampling; also, availability of number of such sets is mostly inadequate for robust training of an ANN so as to obtain realistic inferences subsequently in the prediction phase. Hence, a sparsity-recovery strategy is advocated via a cardinality enhancement procedure (through Nyquist sampling) performed on the sparse data set in order to augment the number of samples in its sampled-data space. Further, the concept of statistical bootstrapping technique of resampling is invoked and applied on the cardinality-improved subset so as to obtain an enhanced number of data sets. This ensemble of data set is then adopted to facilitate robust training of the test Original Research Article British Journal of Economics, Management & Trade, 4(2): 228-263, 2014 229 ANN. Place and Duration of Study: The studies were conducted (2012-2013) at: Department of Computer and Electrical Engineering and Computer Science, College of Engineering & Computer Science, Florida Atlantic University, Boca Raton, Florida 33431, USA. Methodology: The study governs technoeconomic ex ante projections pertinent to a wind-power generation business complex elucidated via ANN-based forecasting. Relevant test ANN is designed to accommodate training with an ensemble of sampled set available ex post but, in limited numbers. The associated scarcity is recovered by artificially enhancing the data space to an adequate extent via Nyquist sampling and bootstrapping techniques. Further, the test ANN designed corresponds to a multilayer perceptron (MLP) supporting backpropagation of the perceived error at the output with respect to a supervisory value. It accommodates the bootstrapped data space at its input relevant to technoeconomic details on a practical wind-power system performance reported in the literature. The training and prediction exercises on the test ANN corresponds to optimally elucidating output predictions in the context of the technoeconomics framework of the power generation considered. Results: Using the test ANN trained with bootstrap-enhanced, scarcity-recovered sparse data on wind-power generation statistics and associated plant economics, reliable inference (in the prediction phase) is achieved on the system performance. That is, the ANN output obtained depicts forecast projections on the productivity of electric power generation in the ex ante regime. Simulation studies thereof and results obtained demonstrate the efficacy of the method proposed, bootstrapping algorithm developed and the use of MLP in the technoeconomic contexts.

Spatial Statistics and Real Estate

Real estate has historically employed statistical tools designed for independent observations while simultaneously noting the violation of these assumptions in the form of clustering of same sign residuals by neighborhood, along roads, and near facilities such as airports. Spatial statistics takes these dependencies into account to provide more realistic inference (OLS has biased standard errors), better prediction, and more efficient parameter estimation. This article provides an overview of the field and directs readers to the relevant literature and software.

Bayesian methods for design and analysis of cost-effectiveness trials in the evaluation of health care technologies.

We review the development of Bayesian statistical methods for the design and analysis of randomized controlled trials in the assessment of the cost-effectiveness of health care technologies. We place particular emphasis on the benefits of the Bayesian approach; the implications of skew cost data; the need to model the data appropriately to generate efficient and robust inferences instead of relying on distribution-free methods; the importance of making full use of quantitative and structural prior information to produce realistic inferences; and issues in the determination of sample size. Several new examples are presented to illustrate the methods. We conclude with a discussion of the key areas for future research.

Neural networks for steady state modelling of an extruder

The inherent complexities of the extrusion process have made the development of both mechanistic and parametric models problematic. This contribution addresses the issues involved in developing a realistic model of an industrial reactive plasticating extruder to enable prediction of extrudate viscosity, which provides a good measure of product quality for the process. The complex nonlinearities associated with the process input-output mapping suggest that neural networks could be an appropriate modelling paradigm. However, the large number of parameters that had to be used caused problems during model identification, since only a limited data set was available. Resampling techniques were therefore used for model identification and validation, due to their efficient use of data and their ability to provide realistic inference of the true error rate associated with the identified models. The statistics obtained are utilised for network structure selection, outlier detection and the derivation of a distribution for model prediction errors. A final network model is presented with fixed confidence bounds, the weights of this network are analysed and an input-output mapping of the process is generated.